<![CDATA[In todays rapidly evolving digital landscape, the success of startups depends heavily on their ability to innovate and form effective partnerships. The process of connecting startups with compatible business partners is crucial, and Machine Learning (ML) has emerged as a promising solution for enhancing this matchmaking. This study utilizes the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to examine attitudes and intentions toward adopting ML for startup matchmaking. Key factors assessed include Performance Expectancy (PE), Ease of Use (EU), Social Influence (SI), and Facilitating Conditions (FC) that affect ML adoption. Using Structural Equation Modeling (SEM), this research analyzes a diverse sample of startups, focusing on variables like Machine Learning Adoption, Data-Driven Matchmaking Strategies, Alignment with Startup Goals, Continuous Learning Integration, and Adaptable Partnerships to evaluate their impact on Matchmaking Efficiency. This study aims to shed light on ML role in enhancing the startup matching process and its overall impact on partnership effectiveness.]]>
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